How to Implement AI in a Hotel or Accommodation Facility

Implementation Methodology

Implementing artificial intelligence in HoReCa requires an approach that considers the industry's specifics and involves advanced technological solutions.

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See how it works

Assisted implementation - with active participation of our team
Iterative implementation - evolution instead of revolution
Specialized RAG technology ensures answer accuracy
Flexibility in LLM selection means compatibility with future models and reliable operation
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Implementations supported by experts

CogniVis is a ready system - you can configure and launch it yourself BUT our implementation methodology assumes close cooperation with your team so you can start using the full capabilities as soon as possible.

The initial configuration of CogniVis resembles a factory machine with an engine and default settings. Such a machine is fully functional - probably one could build a working assembly line around it. However, our implementation methodology assumes that CogniVis adapts to your processes, not the other way around.

Each system element (e.g. Chat Widget, review support, integration with ProfitRoom) is implemented in several stages allowing deep customization.

  1. PREPARATIONS

    Organizing the implementation, scheduling meetings, defining knowledge scopes, identifying your system providers, and exchanging expectations. This stage ends with a preliminary version of the solution being prepared.

    Trial

  2. TESTING

    Initial tests within a narrow group of your employees. We conduct these tests on specially secured channels - we do not interfere with your service line. This stage ends with documented feedback from the Client (comments).

    Trial

  3. CORRECTIONS

    Corrections made by CogniVis based on provided comments. This stage ends with acceptance of corrected solutions and approval for publication.

    Trial

  4. PUBLICATION and MONITORING

    Publication for the target audience (e.g. website users, guests staying at the property, entire internal company team). Enhanced observation of initial results, possible “hotfixes” for non-critical adjustment cases. This stage ends one week after publishing the element.

    SUBSCRIPTION

  5. DEVELOPMENT and SUPPORT

    Continuous development and enhancement of the solution based on observations, changing needs, and technology evolution. At this stage, we also implement new functionalities, more advanced customizations, and integrations with PMS / CRM / HiS / Booking Engine systems and others. This stage lasts as long as you want to use CogniVis.

    SUBSCRIPTION

RAG Search

The foundation of CogniVis technology is the Retrieval-Augmented Generation (RAG) approach, which combines the power of large language models (LLM) with precise information retrieval from your own resources. This allows us to provide answers that are not only accurate but also based on current and verified data. We have been developing and refining our RAG solution since 2023, focusing on the specifics of the hospitality industry.

  • No hallucinations thanks to strict context
  • Full auditability - we always know where the answer comes from
  • Better accuracy thanks to hybrid search and re-ranking
  • Security - permission control and data masking

Search

Vectorization of content and selection of the most relevant fragments from sources.

  • Hybrid search: BM25 + vectors
  • Permission and context filters

Augmentation

Normalization, deduplication and compression - the model gets only what's relevant.

  • Re-ranking and relevance scoring
  • Data leak protection

Generation

Answers created by LLM with optional quoting without hallucinations.

  • Quotes with paragraph numbers
  • Answer mode compliant with company policies

Simplified CogniVis pipeline

  1. 1

    User query

  2. 2

    Normalization and semantic expansion

  3. 3

    Intent recognition, relevance and permission classification

  4. 4

    Hybrid search and re-ranking

  5. 5

    Context merging and compression

  6. 6

    Generating an accurate response or redirecting to a human

Sample response

No hallucinations Based on sources

Every bathroom is equipped with a hairdryer[1]. These hairdryers are usually mounted on the wall or placed in a drawer under the sink[2]. If you need an additional hairdryer or have special requirements, please contact the hotel reception, who will be happy to assist you.

source 1 source 2

*Visible source citations are an optional feature that can be turned off after the trial period. Answers will still be based on sources, but without visible citations.

Flexibility in LLM selection

CogniVis enables integration with various large language model (LLM) providers such as OpenAI, Anthropic, and others. This flexibility allows cost and performance optimization, as well as quick adaptation to technological changes.

Compatibility with future models

We design integrations with the future in mind. When a stronger model appears, you can enable it without system overhaul.

AI advancement = better performance at your property

New generations of models provide more accurate answers, better context understanding, and higher task automation.

No vendor lock-in

If Anthropic releases a model better than OpenAI's, you switch operations with one decision. No content migration and no downtime.

Greater cost efficiency

You choose the provider with the best quality-to-price ratio. Premium models for complex tasks, cheaper ones for routine.

Failure risk reduction

When the primary provider experiences an outage, traffic is switched to an alternative model. Continuity without manual intervention.

Experiments and comparisons

You enable A-B tests and benchmarks between models. Decisions are based on quality, time, and cost metrics.

Supported providers

CogniVis + LLM on your infrastructure

We can deploy both CogniVis and the LLM model locally (on-premise) on your own infrastructure, without the need to use public cloud. This gives you full control over your data and allows you to meet even the most stringent requirements.

OpenAI

Strong language understanding and a stable tool ecosystem. Best for most use cases.

Anthropic
Anthropic

More expensive models focused on precision and maximum accuracy.

Amazon

Wide range of models and integration with AWS. Good if you already use Amazon cloud.

Google

Cheaper, lighter, and faster models with strong multimodality.

Meta

Local models which we deploy directly on your infrastructure, without public cloud.

Microsoft

Models from Azure OpenAI Service offering the highest cloud security standards.

Deepseek

Very cheap and lightweight models that can also run locally, without cloud.

Mistral AI
Mistral

European provider with very competitive pricing and efficient models.

Frequently Asked Questions

What are the key stages of AI implementation in a hotel or accommodation facility?

AI implementation in a hotel or accommodation facility includes several key stages, such as analyzing business needs and goals, auditing knowledge sources, selecting appropriate technologies and providers, configuring and integrating the system, training the team, and monitoring and optimizing AI performance.

How does CogniVis ensure AI response accuracy?

CogniVis AI uses advanced RAG (Retrieval-Augmented Generation) search technologies, Chain-of-Thought reasoning, and other techniques to ensure that AI-generated responses are precise, up-to-date, and based on designated knowledge sources.

Channels

Automatic Responses

AI will immediately answer guest questions, process reservations or offer additional services on these channels.

Instant suggestions

AI will suggest to the employee who can automatically translate, modify or approve it.